Congdong Li

System dynamics analysis for an Internet-of-Things-enabled production logistics system

A production logistics system is often subject to high operational dynamics due to large working areas, frequent resource interactions, long operation periods and intensive human involvement. Researchers have applied system dynamics to design the structure of statistically robust systems which accommodate common dynamics. Yet this approach begins to lose its feasibility because dynamics anticipation and statistics are becoming more difficult in ever more competitive markets and adjustments to system structure typically incur high costs. In response, this study explores how a robust information structure can be designed and real-time control schemes for controlling the dynamics inherent to real-life systems applied. Motivated by the wide application of industrial Internet-of-Things (IoT) systems, this paper investigates the typical production logistic execution processes and adopts system dynamics to design cost-effective IoT solutions. The internal and external production logistic processes are first investigated separately. Using sensitivity analysis, the optimal IoT solutions are evaluated and analysed to provide guidance on IoT implementation. Internal and external production logistic processes are then combined into an integrated structure to offer a generic system dynamics approach. This research does not only enhance the use of system dynamics, but also presents a quantitative IoT system analysis approach.

Internet of Things-based real-time production logistics synchronization mechanism and method toward customer order dynamics:

The reluctance or incapability of further increasing production resources has made many enterprises suffering from high resource-workload situation. Production dynamics thus cannot be resolved by single resource independently, yet have to make an integral use of the adjustable capability of multiple resources of the whole system in a synchronized way. This paper considers a dynamic production logistics (PL) process comprising multiple independently operated PL stages, which adopts Internet of Things to capture real-time execution dynamics and rely on plan (re)scheduling and cloud-based resources re-configuration to cope with dynamics. A generic dynamic production logistics synchronization (PLS) solution is proposed. Qualitatively, a multi-phase multi-stage multi-degree synchronization control mechanism is put forward toward a PL system with generic structure and typical execution dynamics. Quantitatively, collaborative optimization is applied to assist the PLS to obtain the synchronization results. With a real-life case study, the effectiveness of the proposed mechanism and method has been verified. A set of sensitivity analysis is also conducted toward dynamics of different degree and different time, which provides significant managerial implication to PL managers to better deal with dynamics.

On the determination of the optimal configuration of process cluster supply chain by use of augmented Lagrangian coordination

Based on the process of augmented Lagrangian coordination (ALC) solving, a solution framework of ALC for the complex system problem is proposed. Oriented to order fulfillment modes and the process of production outsourcing modes of process manufacturing enterprises in an industrial cluster and according to the hypothesis condition, an optimal configuration model of process cluster supply chain (PCSC) considering order horizontal subcontracting and cross-chain process production outsourcing is set up. By dividing the independent decision-making rights of the participants in single supply chains, a distributed optimal configuration model is established. Meanwhile, ALC is employed to solve the distributed model and solving results are analyzed and compared with the traditional method. Results comparison confirms the accuracy and efficiency of ALC for the PCSC configuration problem.

Optimal configuration of assembly supply chains based on Hybrid augmented Lagrangian coordination in an industrial cluster

Abstract Industrial cluster is becoming an ever more important cost-effective industry development mode especially when enterprises are required to give more rapid responses to the frequently changed customized demands. The explosive number of homogeneous enterprises/suppliers with geographic proximity provides multiple options for each supply chain stage, which thus leads to higher potential to form a more satisfactorily performed assembly supply chain (assembly system) in industrial clusters. However, the increased candidate options also incur inevitably higher decision complexity to the decision model of configuring such cluster supply chains. The situation may be more challenging if the autonomous decision requirement of individual suppliers is accommodated. A general assembly cluster supply chain configuration (ACSCC) model is established which considers both horizontally and vertically collaborations in a cluster, meaning it accommodates the typical cluster relationships including subcontracting and outsourcing. In order to achieve the complexity reduction and autonomy protection, a newly emerged decomposition-based solution method named augmented Lagrangian coordination (ALC) will be adopted. Especially, two classical ALC formulation variants named the centralized coordination formulation and the distributed coordination formulation are innovatively integrated to form a hybrid ALC solution strategy, which deals with different assembly branches with different alliancing structures. Experimental results have proved the effectiveness of the proposed hybrid ALC method for the ACSCC problem. From the perspective of supply chain management, a set of sensitivity analysis for profit of each collaborative enterprise is conducted to obtain some important managerial insights.